# -- coding: utf-8 --
import mnistData
import tensorflow as tf
mnist = mnistData.read_data_sets("MNIST_data/", one_hot=True)
#占位符
x = tf.placeholder(tf.float32, [None, 784])
#参数
W = tf.Variable(tf.zeros([784,10]))
#偏置项
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
#占位符
y_ = tf.placeholder("float", [None,10])

#
def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)

def bias_variable(shape):
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)

#
def conv2d(x, W):
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')

# first
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

#second
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# 定义损失函数来刻画预测值与真实值之间的差距
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
# 定义反向传播算法来优化神经网络中的参数
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

# 预测 0.8.0版本使用第一种会导致评估时可能会内存溢出
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
correct_prediction2 = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
init = tf.global_variables_initializer()
print "beigin"
with tf.Session() as sess:
    sess.run(init)
    for i in range(20000):
    #for i in range(1000):
      print "this is " + str(i + 1) + " train"
      batch = mnist.train.next_batch(50)
      if i%100 == 0:
          train_accuracy = accuracy.eval(feed_dict={
                x:batch[0], y_: batch[1], keep_prob: 1.0})
          print "step %d, training accuracy %g"%(i, train_accuracy)
      train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

    print "train over"
    # 20000 98.79%
    print "test accuracy %g"%accuracy.eval(feed_dict={
    x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1})

# print "over"

# step 900/1600, training accuracy 1
